{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d3f6a2cc-7c7f-44be-8d8e-aaf2c8c7f82f",
   "metadata": {},
   "source": [
    "# Pandas教学\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c1444f26-9ddb-478c-9711-d9e538858ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "47115347-90c5-4abe-9129-ba0b83791135",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时间4点，5点，... 22点\n",
    "times = np.array([6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "# 顾客人数\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "len(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ee35d160-751a-4194-9fb8-631afbb8480f",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  70,  65, 100, 230, 150, 100, 300, 250, 150])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时间4点，5点，... 22点\n",
    "times = np.array([6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "# 顾客人数\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c3933411-29ed-4f18-b3f5-c3165127ca7c",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(times, customers, '.')\n",
    "plt.plot(times, customers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1932ce08-3839-47d2-b0e9-ee7ea9d1a502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6      60\n",
       "7      70\n",
       "8      65\n",
       "11    100\n",
       "12    230\n",
       "13    150\n",
       "18    100\n",
       "19    300\n",
       "20    250\n",
       "21    150\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers =pd.Series([60, 70, 65, 100, 230, 150, 100, 300, 250, 150],\n",
    "                     [6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "\n",
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d86bb34-3e69-4d60-a850-2944ef8b036a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(70)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5de2ba0-8434-487c-9548-911950d508ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6      60\n",
       "7      70\n",
       "8      65\n",
       "11    100\n",
       "12    230\n",
       "13    150\n",
       "18    100\n",
       "19    300\n",
       "20    250\n",
       "21    150\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers =pd.Series([60, 70, 65, 100, 230, 150, 100, 300, 250, 150],\n",
    "                     index=[6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "\n",
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e8be834-79fd-4c5b-bb5b-9f6239110e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(70)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "20d89896-06cb-4ca0-9999-08282a67eb52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6h      60\n",
       "7h      70\n",
       "8h      65\n",
       "11h    100\n",
       "12h    230\n",
       "13h    150\n",
       "18h    100\n",
       "19h    300\n",
       "20h    250\n",
       "21h    150\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times = ['6h', '7h', '8h', '11h', '12h', '13h', '18h', '19h', '20h', '21h']\n",
    "customers = pd.Series([60, 70, 65, 100, 230, 150, 100, 300, 250, 150], index=times)\n",
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a4e5aa5e-8be0-4f31-8493-10edf1a0d1bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(300)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['19h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a998144b-be1f-46e7-aeb6-36e6396e6ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11h    100\n",
       "12h    230\n",
       "13h    150\n",
       "18h    100\n",
       "19h    300\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['11h':'19h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0cdb78e7-cfdd-4f70-a0ff-c54c24ab8a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "880"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(customers['11h':'19h'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "917197c9-5cb6-4b2b-af7a-70223256c9d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "176.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(customers['11h':'19h'])/len(customers['11h':'19h'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "346c9af3-3c6b-47bf-a8ff-1599c11f5644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(customers['11h':'13h'])/len(customers['11h':'13h'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67417036-6637-4e26-9d0c-0d75010cebce",
   "metadata": {},
   "source": [
    "# 生成至少四个同学两门成绩的dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a755882-21e4-4ba3-9cbe-daaf3b3bbbcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "students = ['小明','大明','小王','大王']\n",
    "\n",
    "chinese = np.array([60, 70, 65, 100])\n",
    "\n",
    "math = np.array([ 76, 75, 95, 90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ab95ede-562c-4c01-93cb-18292f826ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>小明</th>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大明</th>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小王</th>\n",
       "      <td>65</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大王</th>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    chinese  math\n",
       "小明       60    76\n",
       "大明       70    75\n",
       "小王       65    95\n",
       "大王      100    90"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'chinese': chinese, 'math': math}\n",
    "df = pd.DataFrame(data, index=students)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e803d3b-6d62-4fc5-93cc-4b9846900126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e6be0ec-6982-49ce-9382-ffbf6699f509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>math</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>小明</th>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大明</th>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小王</th>\n",
       "      <td>65</td>\n",
       "      <td>95</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大王</th>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    chinese  math  total\n",
       "小明       60    76    136\n",
       "大明       70    75    145\n",
       "小王       65    95    160\n",
       "大王      100    90    190"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total'] =df['chinese']+df['math']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "312dbe19-eed0-4839-ba7b-9ab90f8c596d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting demo01.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile demo01.csv\n",
    "restaurant_name,times,meals,customers,costs,notes\n",
    "Xuhui,2018-10-10 06:00:00,breakfast,60,6.6,xx\n",
    "Xuhui,2018-10-10 07:00:00,breakfast,70,7.6,xx\n",
    "Xuhui,2018-10-10 11:00:00,lunch,100,24.5,xx\n",
    "Xuhui,2018-10-10 12:00:00,lunch,230,23.4,xx\n",
    "Xuhui,2018-10-10 20:00:00,dinner,250,35.5,xx\n",
    "Xuhui,2018-10-10 21:00:00,dinner,150,40.6,xx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77f2e15d-0ed2-4d86-8905-4b5aa8f77c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"demo01.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3b6c2c9-8f1e-4c38-95c0-15fbddf564a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_name</th>\n",
       "      <th>times</th>\n",
       "      <th>meals</th>\n",
       "      <th>customers</th>\n",
       "      <th>costs</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Xuhui</td>\n",
       "      <td>2018-10-10 06:00:00</td>\n",
       "      <td>breakfast</td>\n",
       "      <td>60</td>\n",
       "      <td>6.6</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Xuhui</td>\n",
       "      <td>2018-10-10 07:00:00</td>\n",
       "      <td>breakfast</td>\n",
       "      <td>70</td>\n",
       "      <td>7.6</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  restaurant_name                times      meals  customers  costs notes\n",
       "0           Xuhui  2018-10-10 06:00:00  breakfast         60    6.6    xx\n",
       "1           Xuhui  2018-10-10 07:00:00  breakfast         70    7.6    xx"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "488b9477-fd7f-4dd2-a680-d0d258d1d2a1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a72bb579-3867-4368-84f1-4b11d5021a64",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0bd9fd6e-535f-425c-8b5e-cb02d4dd6a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'id1': ['a', 'a', 'a', 'b', 'b', 'b'],\n",
    "    'id2': ['a1', 'a2', 'a2', 'b1', 'b2', 'b2'],\n",
    "    'x': [1, 2, 3, 4, 5, 5] ,\n",
    "    'y': [0.1, 0.3, 0.3, 0.4, 0.5, 0.5] ,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "67f78853-806a-46ff-8a9d-103c343af2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8aa4b6fc-efaf-4113-8868-15c513e66960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id1</th>\n",
       "      <th>id2</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>a1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>a2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>a2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>b1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>b2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>b2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id1 id2  x    y\n",
       "0   a  a1  1  0.1\n",
       "1   a  a2  2  0.3\n",
       "2   a  a2  3  0.3\n",
       "3   b  b1  4  0.4\n",
       "4   b  b2  5  0.5\n",
       "5   b  b2  5  0.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44338b94-f28f-4d4f-b51c-9f8b4a0fd76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id1</th>\n",
       "      <th>id2</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>a1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>a2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>a2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>b1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>b2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id1 id2  x    y\n",
       "0   a  a1  1  0.1\n",
       "1   a  a2  2  0.3\n",
       "2   a  a2  3  0.3\n",
       "3   b  b1  4  0.4\n",
       "4   b  b2  5  0.5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(keep='first',inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "beca2793-a6e8-4110-8786-94d21fe4be91",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d272d937-6435-4445-9173-502748173a40",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('data.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3bfe3239-83f4-4d70-852b-060e6e2d5939",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d834d326-11d0-4b83-ad2c-3e3ff1a56475",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "145    False\n",
       "146    False\n",
       "147    False\n",
       "148    False\n",
       "149    False\n",
       "Length: 150, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d6af0ab6-1d07-45ab-8b21-c8dcab6e42b2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4fd4590c-25a8-423e-a861-fdf722892a28",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sepal_length=df[\"sepal_length\"]\n",
    "sepal_width=df[\"sepal_width\"]\n",
    "species=df[\"species\"]\n",
    "ax = df[df['species']=='setosa'].plot.scatter('sepal_length', 'sepal_width', color='g', label='setosa')\n",
    "ax = df[df['species']=='virginica'].plot.scatter('sepal_length', 'sepal_width', color='r', label='virginica',ax=ax)\n",
    "\n",
    "df.plot.scatter(x='sepal_length',y='sepal_width' ,marker= '.',color='b',label='versicolor',ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d3bebfcc-afb2-4a69-88da-65364cb00722",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "module"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ab68e9d9-7617-4469-933f-694d40005207",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(plt.scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a86470da-b5f2-4211-b083-46928b1bb993",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sepal_length=df[\"sepal_length\"]\n",
    "sepal_width=df[\"sepal_width\"]\n",
    "species=df[\"species\"]\n",
    "ax = df[species=='setosa'].plot.scatter('sepal_length', 'petal_length', color='g', label='setosa')\n",
    "ax = df[species=='virginica'].plot.scatter('sepal_length', 'petal_length', color='r', label='virginica',ax=ax)\n",
    "\n",
    "df.plot.scatter(x='sepal_length',y='petal_length' ,marker= '.',color='b',label='versicolor',ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7e4f2a4a-8e3a-439f-9065-f409cef63fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sepal_length=df[\"sepal_length\"]\n",
    "sepal_width=df[\"sepal_width\"]\n",
    "species=df[\"species\"]\n",
    "ax = df[species=='setosa'].plot.scatter('sepal_width', 'petal_length', color='g', label='setosa')\n",
    "ax = df[species=='virginica'].plot.scatter('sepal_width', 'petal_length', color='r', label='virginica',ax=ax)\n",
    "\n",
    "df.plot.scatter(x='sepal_length',y='petal_length' ,marker= '.',color='b',label='versicolor',ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "be6e7cc3-56fd-4183-9ada-b1caa7e0e4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sepal_length=df[\"sepal_length\"]\n",
    "sepal_width=df[\"sepal_width\"]\n",
    "species=df[\"species\"]\n",
    "ax = df[species=='setosa'].plot.scatter('sepal_length', 'petal_width', color='g', label='setosa')\n",
    "ax = df[species=='virginica'].plot.scatter('sepal_length', 'petal_width', color='r', label='virginica',ax=ax)\n",
    "\n",
    "df.plot.scatter(x='sepal_length',y='petal_length' ,marker= '.',color='b',label='versicolor',ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a86e4c3a-e2a0-4baa-b0a5-64d09b7ec8fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
